Claude-skill-registry competitive-price-analysis-pipeline

Performs competitive price analysis by comparing internal pricing data with competitor information from different sources (PDFs, CSVs, spreadsheets), calculates price differences, generates insights, and stores results in BigQuery for decision-making.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/competitive-price-analysis-pipeline" ~/.claude/skills/majiayu000-claude-skill-registry-competitive-price-analysis-pipeline && rm -rf "$T"

manifest: skills/data/competitive-price-analysis-pipeline/SKILL.md

Instructions

1. Initial Setup & Discovery

List the workspace directory to identify available files.
Read the requirements document (e.g.,
```
requirement.md
```
) to understand the target BigQuery dataset, table, and required schema.
Read the internal pricing file (CSV or Excel) to understand the structure and product list.

2. Extract Competitor Pricing Data

Inspect the competitor PDF to understand its structure (page count, layout).
Read all pages of the competitor PDF to extract text content containing product names and prices.

3. Data Processing & Matching

Parse the internal pricing data. The key columns are
```
Product Name
```
and
```
Our Price
```
.
Parse the competitor PDF text. Identify product names and their corresponding prices. Use fuzzy matching or keyword mapping (e.g., "SmartWidget Pro" → "SmartWidget Professional Edition") based on the trajectory example.
Create a matched product list. For each internal product, find the corresponding competitor product and price. If a direct match isn't found, log it for manual review.
Calculate the price difference for each matched product:
```
Price Difference = Our Price - Competitor Price
```
.

4. Prepare Output & Load to BigQuery

Create a CSV file (
```
price_comparison.csv
```
) with the exact columns specified in the requirements:
- ```
Product Name
```
  (String)
- ```
Our Price
```
  (Float)
- ```
Competitor Price
```
  (Float)
- ```
Price Difference
```
  (Float)
Verify the BigQuery dataset exists. If it doesn't, you may need to create it (though the trajectory shows it pre-existing).
Load the CSV data into the specified BigQuery table (
```
dataset_id.table_id
```
). Use
```
WRITE_TRUNCATE
```
mode to replace any existing data, as per the trajectory.
Run a validation query to confirm the data was loaded correctly and matches the expected row count and schema.

5. Generate Summary & Finalize

Provide a concise summary of the analysis, including:
- Number of products processed.
- Count of products priced higher/lower than the competitor.
- The product with the largest price advantage (most negative difference).
- The product with the largest price disadvantage (most positive difference).
Claim the task as done.

Key Considerations

Product Matching Logic: This is a high-freedom, interpretive step. Use the provided
```
scripts/match_products.py
```
as a starting point, but be prepared to adjust matching rules (keywords, categories) based on the specific data.
BigQuery Permissions: Ensure the agent has the necessary permissions to list datasets and load data to the specified table.
Data Types: Ensure numeric prices are parsed as floats. Handle currency symbols and commas appropriately.
Error Handling: If the PDF extraction yields poor results, consider using the
```
pdf-tools-search_pdf
```
function to find specific product names or prices.